Medical Open Network for AI for AMD ROCm™
MONAI for AMD ROCm™ is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the PyTorch Ecosystem, enabled for AMD Instinct GPUs.
Its ambitions are as follows:
- Developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
- Creating state-of-the-art, end-to-end training workflows for healthcare imaging;
- Providing researchers with the optimized and standardized way to create and evaluate deep learning models.
Please see the technical highlights and What's New of the milestone releases.
- flexible pre-processing for multi-dimensional medical imaging data;
- compositional & portable APIs for ease of integration in existing workflows;
- domain-specific implementations for networks, losses, evaluation metrics and more;
- customizable design for varying user expertise;
- multi-GPU multi-node data parallelism support.
MONAI for AMD ROCm works with Python 3.10, and depends directly on NumPy and PyTorch for AMD ROCm with many optional dependencies.
- AMD MONAI supports ROCm-LS/hipCIM for accelerated image loading and processing on AMD Instinct GPUs.
- See the
requirements*.txtfiles for dependency version information.
To install the current release, you can simply run:
pip install amd_monai --extra-index-url=https://pypi.amd.com/simplePlease refer to the installation guide for other installation options.
MedNIST demo and MONAI for PyTorch Users are available on Colab.
Examples and notebook tutorials are located at Project-MONAI/tutorials.
Technical documentation is available at MONAI for AMD ROCm documentation.
The MONAI Docker image is available from Dockerhub,
tagged as latest for the latest state of dev or with a release version. A slimmed down image can also be built
locally using Dockerfile.slim, see that file for instructions.
To get started with the latest MONAI, use docker run -ti --rm --gpus all projectmonai/monai:latest /bin/bash.
If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.
The MONAI Model Zoo is a place for researchers and data scientists to share the latest and great models from the community. Utilizing the MONAI Bundle format makes it easy to get started building workflows with MONAI.
For guidance on making a contribution to MONAI, see the contributing guidelines.
Join the conversation on Twitter/X @ProjectMONAI, LinkedIn, or join our Slack channel.
Ask and answer questions over on MONAI's GitHub Discussions tab.
- Website: https://instinct.docs.amd.com/latest/life-science/MONAI.html
- Code: https://github.com/ROCm-LS/MONAI
- Issue tracker: https://github.com/ROCm-LS/MONAI/issues
- PyPI package: https://pypi.amd.com/simple/amd-monai/